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4 weeks ago | bmcmededuc.biomedcentral.com | Kehinde O. Sunmboye |Samina Noorestani |Hannah Strafford |Malena Wilison-pirie

With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI tools, can adapt to individual learning styles and needs, thus transforming how medical students approach their studies. This study aims to explore the relationship between the use of AI for self-directed learning among undergraduate medical students in the UK and variables such as year of study, gender, and age. This cross-sectional study involved a sample of 230 undergraduate medical students from UK universities, collected through an online survey. The survey assessed AI usage in self-directed learning, including students’ attitudes towards AI accuracy, perceived benefits, and willingness to mitigate misinformation. Data were analyzed using descriptive statistics and linear logistic regression to examine associations between AI usage and demographics. The analysis revealed that age significantly influenced students’ willingness to pay for AI tools (p = 0.012) and gender was linked to concerns about AI inaccuracies (p = 0.017). Female students were more likely to take steps to mitigate risks of misinformation (p = 0.045). The study also found variability in AI usage based on the year of study, with first-year students showing a higher reliance on AI tools. AI has the potential to greatly enhance personalized learning for medical students. However, issues surrounding accuracy, misinformation, and equitable access need to be addressed to optimize AI integration in medical education. Further research is recommended to explore the longitudinal effects of AI usage on learning outcomes.

2 months ago | bmcmededuc.biomedcentral.com | Jung-Hee Bae |Jae-Gi Lee |Ji-eun Im |Ja-young Gu

In dental radiography education, students typically observe instructor demonstrations and practice on mannequins or peers. However, owing to the large student-to-instructor ratio, providing individualized feedback is challenging. Repeated practice is also hindered by radiation exposure from dental radiography machines. Implementing three-dimensional (3D) object-based virtual reality (VR) simulations can address these concerns. We developed a 3D object-based VR-simulation tool for dental radiography learning (namely, 3DOVR-DR) and evaluated user experiences. For the development of 3DOVR-DR, a virtual dental radiography room was constructed using 3D objects. The intraoral radiography process was divided into 12 steps, and the Unity 3D engine was used to create an interactive VR environment for step-by-step learning. This study was a randomized controlled trial. To evaluate user experience, 79 participants were randomly assigned to a control group (n = 39), which used Google Cardboard for VR, or an experimental group (n = 40), which used 3DOVR-DR, to evaluate the user experience. A survey questionnaire of 22 items was administered to all participants. Statistical analyses included descriptive statistics and Mann–Whitney U test. The 3DOVR-DR tool provided an immersive experience for simulating and learning the dental radiography process within a VR setting. Users performed step-by-step tasks related to dental radiography in the virtual space, adjusting and repeating the entire process or specific steps as needed for their learning. Users received guidance and practiced dental radiography using 3DOVR-DR. User-experience ratings were significantly higher in the experimental group (4.35±0.47) compared to the control group (3.63±0.66; P < 0.001). The 3DOVR-DR tool shows potential as a learning medium for intraoral radiography education. Further analysis is needed to examine the impact and mediating effects of the 3D object-based VR experience on dental radiographic practice. Future research should include pedagogical analysis to evaluate the educational effectiveness of this learning tool.

2 months ago | bmcmededuc.biomedcentral.com | Sana Saeed |Sobia Ali |Azam Afzal |Marib Ghulam Rasool Malik |Muhammad Ahsan Naseer

The integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to evaluate knowledge, attitudes, and practices of medical students and faculty regarding AI in medical education, and explore the perceptions and key barriers regarding strategies for effective AI integration. A concurrent mixed-methods study was conducted over six months (July 2023 to January 2024) at a tertiary care medical college in Pakistan. The quantitative component utilized a cross-sectional design, with 236 participants (153 medical students and 83 faculty members) completing an online survey. Mean composite scores for knowledge, attitudes, and practices were analyzed using non-parametric tests. The qualitative component consisted of three focus group discussions with students and six in-depth interviews with faculty. Thematic analysis was performed to explore participants’ perspectives on AI integration. Majority of participants demonstrated a positive attitude towards AI integration. Faculty had significantly higher mean attitude scores compared to students (3.95 ± 0.63 vs. 3.81 ± 0.75, p = 0.040). However, no statistically significant differences in knowledge (faculty: 3.53 ± 0.66, students: 3.55 ± 0.73, p = 0.870) or practices (faculty: 3.19 ± 0.87, students: 3.23 ± 0.89, p = 0.891) were found. Older students reported greater self-perceived knowledge (p = 0.010) and more positive attitudes (p = 0.016) towards AI, while male students exhibited higher knowledge scores than females (p = 0.025). Qualitative findings revealed key themes, including AI’s potential to enhance learning and research, concerns about over-reliance on AI, ethical issues surrounding privacy and confidentiality, and the need for institutional support. Faculty emphasized the importance of training to equip educators with the necessary skills to effectively integrate AI into their teaching. This study highlights both the enthusiasm for AI integration and the significant barriers that must be addressed to successfully implement AI in medical education. Addressing technological constraints, providing faculty training, and developing ethical guidelines are critical steps toward fostering the responsible use of AI in medical curricula. These findings underscore the need for context-specific strategies, particularly in resource-limited settings, to ensure that medical students and educators are well-prepared for the future of healthcare.

Jan 17, 2025 | bmcmededuc.biomedcentral.com | Abdul Latif Sami |Dilber Uzun Ozsahin |Khalid Muhammad |Muhammad Ahsan Javed |Fateema Tanveer |Yasir Waheed | +2 more

The rapid advancement of artificial intelligence (AI) has revolutionized both medical education and healthcare by delivering innovative tools that enhance learning and improve overall outcomes. The study aimed to assess students’ perceptions regarding the credibility and effectiveness of AI as a learning tool and to explore the dynamics of integrating AI in medical education. A cross-sectional study was carried out across medical colleges in Pakistan. A 26-question survey was developed using Google Forms from previously validated studies. The survey assessed demographics of participants, basic understanding of AI, AI as a learning tool in medical education and socio-ethical impacts of the use of AI. The data was analyzed using SPSS (v 26.0) to derive descriptive and inferential statistics. A total of 702 medical students aged 18 to 26 years (mean age 20.50 ± 1.6 years) participated in the study. The findings revealed a generally favorable attitude towards AI among medical students (80.3%), with the majority considering it an effective (60.8%) and credible (58.4%) learning tool in medical education. Students agreed that AI learning optimized their study time (60.3%) and provided up-to-date medical information (63.1%). Notably, 65.7% of students found AI more efficient in helping them grasp medical concepts compared to traditional tools like books and lectures, while 66.8% reported receiving more accurate answers to their medical inquiries through AI. The study highlighted that medical students view traditional tools as becoming increasingly outdated (59%), emphasizing the importance of integrating AI into medical education and creating dedicated AI tools (80%) for the medical education. This study demonstrated that AI is an effective and credible tool in medical education, offering personalized learning experiences and improved educational outcomes. AI tools are helping students learn medical concepts by cutting down on study-time, providing accurate answers, and ultimately improving study outcomes. We recommend developing dedicated AI tools for medical education and their formal integration into medical curricula, along with appropriate regulatory oversight to ensure AI can enhance human abilities rather than acting as a replacement for humans.

Nov 15, 2024 | bmcmededuc.biomedcentral.com | Sayaka Oikawa |Yayoi Shikama |Megumi Yasuda |Maham Stanyon |Koji Otani

Clinical leadership competencies for effective teamwork differ between Western cultures, where an independent self-construal prevails, and Japanese society, where the self-construal is rooted in interdependence. Although 27 out of 82 Japanese medical schools have ‘leadership’ as an educational outcome, specific competencies are poorly described, hindering the development of contextually-relevant leadership education. This study aimed to identify clinical leadership competencies and articulate the attributes and skills fundamental to leadership as perceived by Japanese physicians. The 80 items of the UK clinical leadership competency framework (CLCF) formed the stimulus in a modified Delphi. Participants, comprising 26 Japanese physicians, rated the importance of each item using a 5-point Likert scale with free-text comments regarding the modification of competencies and suggestions for new items. Items were eliminated if the Likert mean was less than 4.0 and if fewer than 70% of participants considered them to be important. Newly described or modified items derived from free-text comments were rated for importance in a second round with reflective thematic analysis of the free-text descriptions. A CLCF of 84 items, reflective of Japanese clinical leadership, was created by eliminating three items describing tasks rarely involving Japanese physician leaders, revising seven items to emphasize understanding of members, and adding seven items to maximize feelings of team comfort. Seven skills and attributes emerged to construct Japanese clinical leadership from thematic analysis. “Humility” was viewed as a fundamental to leadership. Humility-driven “self-discipline” and “attentive listening”, “supporting members” and “guiding members” with humility-based compassion, were essential elements to create “psychological safety” for freedom of expression. Achieving “unity” through emotional integration was identified as the overall goal of leadership. The reorganized CLCF has embedded more member-centered behaviors that build rapport and comfort for the members than the original CLCF. Modeling the Confucian virtue of humility and building unity by acting with compassion toward members are characteristics of Japanese clinical leadership that reflect an interdependent social context. These findings are a step toward the development of leadership education aligned with a Japanese context.